جست‌وجوی استاد و پژوهشگر

• Mass Spectrometry
• Imaging Mass Spectrometry
• MALDI Imaging
• +۴ مورد دیگر

Raf Van de Plas is a researcher at Delft University of Technology specializing in mass spectrometry imaging and data analysis. His work focuses on developing advanced techniques for molecular analysis of biological tissues, particularly in kidney research and lipidomics. With a PhD in Mechanical Engineering, he bridges engineering principles with biomedical applications. His research interests include: Mass Spectrometry Imaging MALDI Imaging Techniques Lipid Analysis and Mapping Kidney Tissue Molecular Profiling Computational Data Analysis Biomolecular Imaging Algorithms Dr. Van de Plas has published extensively in high-impact journals with multiple 2025 publications in Science Advances, Nature Communications, Kidney International, and Analytica Chimica Acta. His work demonstrates a clear trajectory toward developing innovative computational workflows for mass spectrometry data while applying these techniques to solve critical biomedical problems, particularly in kidney pathology and lipid metabolism. His scientific contributions include: Development of advanced MALDI matrices for high spatial resolution imaging Creation of msiFlow automated analysis workflows Comprehensive lipid mapping of human kidney tissues Integration of mass spectrometry with immunofluorescence microscopy Dr. Van de Plas actively supervises research work and collaborates internationally across biomedical and engineering disciplines, with significant contributions to both methodological development and clinical applications of imaging mass spectrometry.

Michael Williamsمشاهده پروفایل

پژوهشگر ارشد

• Gravitational Wave Physics
• Neutron Stars
• Black Holes
• +۳ مورد دیگر

Dr. Michael Williams is a Research Fellow at the Institute of Cosmology & Gravitation under the Faculty of Technology , University of Portsmouth. His work focuses on gravitational wave physics, neutron star dynamics, and black hole binaries through collaborations with LIGO, Virgo, and KAGRA detector networks. Active in gravitational wave detection and multi-messenger astronomy Developing AI algorithms for telescope targeting and signal processing Recipient of the IOP Trusted Reviewer status (2025) His recent research includes premerger characterization of black hole binaries , continuous wave searches from pulsars , and gravitational wave-FRB counterpart detection . He contributes to open-source tools like nessai and bayesbeat , with 25 datasets released. Williams participates in conferences like the European AI for Fundamental Physics Conference and workshops on milli-hertz gravitational wave astrophysics. Key collaborations: LIGO, Virgo, KAGRA, GEO600 Technical expertise: matched filtering , Bayesian inference , normalizing flows

Iwan Schieمشاهده پروفایل

استاد

• Raman Spectroscopy
• Biomedical Imaging
• Cancer Diagnostics
• +۳ مورد دیگر

Iwan Schie serves as Working Group Leader at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany, where he leads the Spectroscopy / Imaging Multimodal Instrumentation research group. His work bridges analytical chemistry, biomedical engineering, and clinical applications with a focus on developing Raman spectroscopy-based diagnostic tools. Dr. Schie maintains an active research program with numerous publications in high-impact journals across multiple disciplines. Dr. Schie's research centers on Raman spectroscopy applications in medical diagnostics and environmental monitoring. His work demonstrates particular expertise in developing multimodal imaging systems that combine Raman spectroscopy with complementary techniques like optical coherence tomography and fluorescence imaging. His research spans both fundamental methodological development and clinical translation, with several studies focusing on cancer diagnostics across multiple organ systems including head and neck, bladder, and colon cancers. The environmental applications of his work include microplastic detection and pollen analysis. Analysis of Dr. Schie's publication record reveals a clear trajectory toward clinical implementation of Raman spectroscopy technologies. His recent work increasingly focuses on regulatory-compliant medical device development, with multiple studies conducted in accordance with European Medical Device Regulation standards. The publications demonstrate progression from ex vivo validation studies to in vivo clinical applications, with particular emphasis on workflow integration within surgical settings. His collaborative approach is evident through extensive co-authorship networks spanning physics, engineering, and clinical medicine. Dr. Schie has made significant contributions to advancing Raman spectroscopy methodology, with publications addressing critical challenges in device stability, spectral analysis, and multimodal integration. His work on establishing clinical workflows represents important steps toward routine clinical adoption of these technologies. The practical impact of his research is demonstrated through development of systems like the invaScope Raman endoscopy platform for bladder tumor diagnosis. As Working Group Leader at Leibniz-IPHT, Dr. Schie oversees research activities in spectroscopy and multimodal imaging instrumentation. His team develops advanced optical systems for biomedical applications with particular focus on real-time tissue characterization during surgical procedures. The research environment supports both fundamental methodological development and applied clinical translation, with strong emphasis on regulatory compliance for medical device development.

Rebecca J. Whelanمشاهده پروفایل

دانشیار

• Bioanalytical Chemistry
• Proteomics
• Microscale Separations
• +۷ مورد دیگر

Rebecca J. Whelan is an Associate Professor in the Department of Chemistry at the University of Kansas, specializing in bioanalytical chemistry. With appointments in both the Chemistry and Bioengineering programs, her research focuses on ovarian cancer biomarker analysis through proteomics, microscale separations, and affinity reagent development. B.A. Chemistry and English, Lawrence University (1996) Ph.D. Chemistry, Stanford University (2003) Postdoctoral Research Fellow, Bioanalytical Chemistry, University of Michigan (2003-2004) Research Interests: The Whelan Lab develops innovative analytical methods for cancer biomarker characterization, including: Structural analysis of CA125 (MUC16) using long-read sequencing and mass spectrometry Development of DNA aptamers as artificial antibodies for biomarker detection Microscale separation techniques (capillary electrophoresis) for clinical proteomics Biosensor development for point-of-care diagnostics Integration of bioinformatics with experimental validation Sample preparation methods for glycoprotein analysis Scientific Awards: Vice Chair-Elect, Gordon Research Conference on Bioanalytical Sensors (2022) Henry Dreyfus Teacher-Scholar Award (2011) W.M. Keck Foundation Fellowship (2008) Research Corporation, Cottrell College Science Award (2006) Walter J. Gores Award for Teaching Excellence (1999) Teaching and Funding: Whelan has received multiple teaching honors including the Professor Props Award finalist. Her lab receives funding from the National Cancer Institute, National Science Foundation, and Dreyfus Foundation. She co-directed the T32 Chemical Biology training program and received the Newmark Award for biochemical research excellence.

Abhraneel Sarmaمشاهده پروفایل

پژوهشگر

• Uncertainty Visualization
• Data Analysis
• Multiverse Analysis
• +۳ مورد دیگر

Abhraneel Sarma is a PhD candidate in Computer Science at Northwestern University, advised by Professors Matthew Kay and Jessica Hullman. His research focuses on addressing uncertainty in data analysis through tools like multiverse (an R package for sensitivity analysis) and Milliways (a visualization system for multiverse analysis validation). Uncertainty visualization Multiverse analysis Bayesian inference Reproducibility in research His work combines empirical studies and system development to improve data-driven decision-making. Publications include best paper awards at CHI 2019 and honorable mentions at CHI 2024, CHI 2023, and VIS 2022. 2025 : CHI 2024 : CHI (Best Paper Honorable Mention) 2023 : CHI (Best Paper Honorable Mention) 2022 : VIS (Best Paper Honorable Mention) 2019 : CHI (Best Paper Award) Email: abhraneel@u.northwestern.edu

Eugene H. Spaffordمشاهده پروفایل

استاد

• Information Security
• Cybersecurity
• Computer Forensics
• +۲ مورد دیگر

Dr. Eugene H. Spafford ("Spaf") is a Distinguished Professor in the Department of Computer Sciences at Purdue University, serving since 1987. He holds courtesy appointments in Philosophy, Communication, Electrical and Computer Engineering, Nuclear Engineering, and Political Science. As founder and Executive Director Emeritus of CERIAS and the COAST Laboratory, he has shaped information security research and education. His research centers on information security, computer crime investigation, and information ethics. Spafford's work in cybersecurity, computer forensics, and computing ethics has established him as a leading figure in the field. He emphasizes practical security solutions and ethical considerations in technology. Recent publications address cybersecurity myths, deception-based security techniques, and password security, alongside memorials for computing pioneers. This blend reflects his dual focus on advancing security research and preserving the field's history. Spafford has advised numerous graduate students and secured significant funding for CERIAS, fostering interdisciplinary collaboration. He currently serves as Editor-in-Chief of Computers & Security and on the board of the Computing Research Association. Leading CERIAS, Spafford built one of the world's premier academic centers for information assurance, integrating expertise from computer science, engineering, and social sciences to address complex security challenges.

Mikkel Wallentinمشاهده پروفایل

استاد

• Psycholinguistics
• Neurolinguistics
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Mikkel Wallentin is a Professor at the School of Communication and Culture, Aarhus University, specializing in Cognitive Science. He investigates the intricate relationships between language, brain, and cognition through interdisciplinary research combining neuroscience, linguistics, and psychology. His research interests include psycholinguistics, neurolinguistics, neuropragmatics, spatial memory, language comprehension, and inner speech . He employs methods such as fMRI, behavioral experiments, and corpus analysis to explore how cognitive processes are embodied in linguistic behavior. His work also touches on gender differences in language development, depression detection via lexical choice, and aging in spatial communication. Wallentin's recent publications reveal a strong focus on emotional inner speech, its physiological effects, and its role in self-regulation , alongside studies on early language acquisition and linguistic pragmatics. His work bridges theoretical linguistics with cognitive neuroscience, often involving cross-linguistic and clinical populations. Principal Investigator, SCALA: Spatial Communication and Ageing across Languages (2024–2028) Participant, Cognition, Language and Music (2009–2013) Participant, Mass Experiment 2016 He has been actively involved in public dissemination, delivering lectures such as 'Language: A Window into the Mind' and contributing to media on topics like julefilm psychology and developmental language disorders. With over 127 research outputs, he maintains a robust scholarly presence in cognitive science.

Marc Boyerمشاهده پروفایل

استاد

• Formal Methods
• Network Calculus
• Real-Time Systems
• +۷ مورد دیگر

Marc Boyer is a Research Director at ONERA, the French Aerospace Lab, within the Department of Aerospace and Information Systems Technologies (DTIS). He is actively affiliated with the Université de Toulouse, where he teaches at the M2 level in computer science and engineering programs at UPS and ENSEEIHT. His research lies at the intersection of formal methods, real-time systems, and embedded networking, with a strong focus on network calculus and Time-Sensitive Networking (TSN). Institution: ONERA / DTIS Teaching Affiliations: Université de Toulouse (UPS, ENSEEIHT) Doctoral School: MITT (Mathematics, Computer Science, Telecommunications) His research interests include formal verification of real-time networks, worst-case performance evaluation, scheduling models, and deterministic networking. He has made significant contributions to the theoretical and practical application of network calculus in avionics (AFDX), automotive, and space systems. His work bridges theory and industry applications, particularly in safety-critical domains. His recent publications (2020–2024) show a consistent focus on refining network calculus models for TSN, CQF, credit-based shaping, and sporadic traffic integration. These works are published in top-tier venues such as ECRTS, RTAS, ETFA, and IEEE Transactions, indicating high impact and relevance in real-time systems research. Best Paper Award, JRWRTC 2023 Best Paper Award, ICATPN 2007 Co-chair, RTNS 2023 Program Committee Active in ECRTS, RTAS, DATE, ETFA program committees Marc Boyer supervises multiple PhD students and offers internships through the MITT doctoral school. He leads the MOIS doctoral reception team and is involved in industrial challenges like Resilient TSN. He has developed tools like NC-maude for formal analysis and contributes to open research datasets and reproducibility. His work combines theoretical rigor with practical implementation, influencing both academic and industrial standards in deterministic networking. He is also active in educational outreach, delivering lectures on digital transformation and cognitive biases in engineering, and promoting tools like Emacs for productivity.

• Neuromorphic Computing
• Spiking Neural Networks
• Bio-inspired Artificial Intelligence
• +۸ مورد دیگر

Dr James Knight is a Senior Research Fellow in Informatics at the School of Engineering and Informatics, University of Sussex. He is currently an EPSRC Research Software Engineering Fellow (2022–2026), leading research in neuromorphic computing and spiking neural networks. His work bridges computer science, neuroscience, and robotics, with a focus on bio-inspired artificial intelligence and computational modeling of insect cognition. Education: PhD in Computer Science, University of Manchester (2013–2016) MPhil in Advanced Computer Science, University of Cambridge (2012–2013) BEng in Electronic Engineering, University of Warwick (2003–2006) His research interests center on neuromorphic computing , spiking neural networks , and bio-inspired AI , particularly drawing insights from insect neuroscience to develop efficient and robust machine learning systems. He explores event-based vision, neural hardware acceleration, and computational models of insect navigation and perception. The 15 most recent publications (2024–2025) reflect a strong trend in developing and benchmarking neuromorphic algorithms, including training methods like Eventprop, hardware-software co-design (e.g., FeNN processor), and modeling biological systems such as insect central complex and mushroom bodies. These works span journals like Nature Communications , PLoS Computational Biology , and Neuromorphic Computing and Engineering , as well as IEEE and Springer conferences. Scientific Grants: Unlocking spiking neural networks for machine learning research (EPSRC, 2022–2026) – Principal Investigator ActiveAI – active learning and selective attention for robust, transparent and efficient AI (EPSRC, 2019–2022) – Research Team Member Dr Knight actively collaborates with leading researchers including Thomas Nowotny, Andrew Philippides, and Paul Graham. His work contributes to both theoretical advances in neural computation and practical applications in robotics and AI. He is involved in developing frameworks such as NeuroBench for standardized evaluation of neuromorphic systems. Laboratories and Research Teams: He is part of the neuromorphic computing and computational neuroscience research groups at the University of Sussex, contributing to interdisciplinary projects that integrate software engineering, AI, and biological modeling.

• Natural Language Generation
• Natural Language Processing
• Low-Resource Language Modeling
• +۱۲ مورد دیگر

David M. Howcroft is an Advanced Research Fellow in Natural Language Generation at the School of Natural and Computing Sciences, University of Aberdeen . He previously held research fellowships at Edinburgh Napier University, Heriot-Watt University, and Saarland University, contributing to major NLP projects including ASICA, NLG for Low-Resource Domains, and Madrigal. His work bridges computational linguistics, psycholinguistics, and statistical modeling. His research focuses on natural language generation , particularly in low-resource settings . He develops machine learning methods for data-to-text generation, creates novel corpora (e.g., for Scottish Gaelic), and improves human evaluation practices via crowdsourcing and rigorous statistical analysis. A key interest is the application of Bayesian nonparametrics and ordinal mixed-effects models to better understand and evaluate generated text. He also explores readability, referring expressions, and AI planning for rule-based NLG systems. His recent publications reveal a strong trend toward methodological rigor and inclusivity in NLP. He advocates for better evaluation standards, transparency in metric usage, and participatory design in NLP research. His work spans corpus development , evaluation methodology , low-resource language support , and human-centered NLP . He has led efforts to create datasets for under-resourced languages and to standardize best practices in human assessment. He has received small grant funding for projects such as Scottish Gaelic Generation for Exhibits and has contributed to software tools for data collection and evaluation. He mentors and collaborates widely, though no formal advisees are listed. He has developed backend systems and Android apps for healthcare applications, notably in melanoma patient support via the ASICA project. He is actively involved in research labs and teams including: ASICA Project Team (University of Aberdeen) NLG for Low-Resource Domains (Edinburgh Napier University) Madrigal Project (Heriot-Watt University) SFB 1102 Project A4 (Saarland University) Language Science and Technology (LSV, Saarland University) His scientific contributions are widely disseminated through top-tier venues such as ACL, EMNLP, and INLG. He maintains an active online presence with tutorials and technical blog posts on tools like OpenCCG and Treex.

• Fluid Dynamics
• Turbulence
• Large Eddy Simulation
• +۱۳ مورد دیگر

Lian Shen is a Professor in the Department of Mechanical Engineering at the University of Minnesota and serves as the Director of the St. Anthony Falls Laboratory, a premier research center for fluid mechanics and environmental engineering. He is actively involved in interdisciplinary research with strong ties to atmospheric science, oceanography, and renewable energy systems. Position: Professor, Mechanical Engineering Leadership: Director, St. Anthony Falls Laboratory Institution: University of Minnesota, College of Science and Engineering His research focuses on fundamental and applied aspects of fluid dynamics, particularly turbulence, air-sea interaction, and environmental flows. Using advanced computational techniques such as Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), he investigates complex phenomena including marine atmospheric boundary layers, upper-ocean turbulence, floating offshore wind systems, and biofilm-sediment interactions. His work integrates high-performance computing, machine learning, and field data validation to address challenges in climate modeling and sustainable energy. The trends in his recent publications (2018–2025) show a consistent emphasis on computational modeling of turbulent flows influenced by waves, stratification, and biological factors. His articles span journals in fluid mechanics, geophysics, and applied mathematics, reflecting a highly interdisciplinary approach. Key themes include GPU-accelerated simulations, wind-wave generation theory, particle-laden convection, and the role of synthetic biofilms in sediment evolution. Dr. Shen has secured substantial funding from federal agencies including the U.S. Department of Defense (Navy), the U.S. Department of Energy, and the National Renewable Energy Laboratory. His active grants support projects such as: LES of moisture and aerosol in marine atmosphere with air-sea interaction Fundamental dynamics of upper-ocean turbulence Modeling bubble dynamics at field sites FLOWMAS: Floating Offshore Wind Modeling and Simulation Impacts of biofilms on seabed topography He advises postdoctoral researchers and graduate students, fostering the next generation of scientists in fluid mechanics and environmental engineering. His lab produces open datasets supporting transparency and reproducibility in research.

Christopher Hendonمشاهده پروفایل

دانشیار

• Computational Chemistry
• Materials Science
• Energy Storage
• +۹ مورد دیگر

Christopher Hendon is an Associate Professor in the Department of Chemistry and Biochemistry at the University of Oregon, where he leads research in computational chemistry, sustainable materials, and energy storage. He is a key member of the university’s Energy and Sustainable Materials Initiative, promoting interdisciplinary collaboration to address global sustainability challenges. Department: Department of Chemistry and Biochemistry School: College of Arts and Sciences University: University of Oregon Lab: HMS Oregon – located in the Lewis Integrative Sciences Building Education: BSc (Advanced Hons) from Monash University, PhD from the University of Bath, Postdoc at MIT Hendon’s research spans computational modeling of metal-organic frameworks (MOFs), defect chemistry in semiconductors, and the physics of coffee extraction. He uses quantum chemical simulations to study electron transport, doping, and catalytic mechanisms, with applications in batteries, hydrogen storage, and carbon capture. His work in coffee science has revolutionized understanding of water chemistry, grinding, and brewing reproducibility, influencing both industry and academic communities. The recent articles from his group reflect a strong interdisciplinary trend, combining chemistry, biology, environmental science, and sensory analysis. Key themes include coffee quality, food safety (e.g., mycotoxins), microbiome interactions, packaging, and neurocognitive effects of caffeine. The research increasingly integrates data science, consumer behavior, and sustainability metrics. Scientific Engagement: Author of Water for Coffee , a foundational text in specialty coffee Collaborator with World Barista Champions (Maxwell Colonna-Dashwood, Kyle Ramage) COHOP research collaboration in coffee science Developer of a proposed interdisciplinary coffee research lab at UO Hendon actively mentors graduate and undergraduate students and collaborates with experimentalists worldwide. His lab does not handle traditional chemicals but uses supercomputers for quantum simulations. While he currently cannot support postdoctoral scholars due to funding limitations, he encourages applications from motivated graduate students through the UO Chemistry Department. His outreach through coffee has made complex scientific concepts accessible to broad audiences. Lab and Research Team: Graduate Students: Tekalign T. Debela, Parker Brodale, Doran Pennington, Brian Diamond, Casey Clark Undergraduates: Jasper Sterling, Julio Miranda, Maggie French, Laurel Wood, Ian Reynolds, Octavio Solórzano Visiting Scholar: Matthew Sciprint Focus Areas: MOFs, defect engineering, energy materials, coffee science

Coen De Rooverمشاهده پروفایل

استاد

• Program Analysis
• Software Quality
• Static and Dynamic Analysis
• +۷ مورد دیگر

Coen De Roover is a Professor at the Software Languages Lab of the Vrije Universiteit Brussel , actively leading research in program analysis, software quality, and security. He chairs the Bachelor in Computer Science program and supervises a dynamic research group. Research Focus: Static/dynamic analysis, automated testing, software maintenance, AI for SE, infrastructure as code security. Projects: Bugatti (2025-2028), Cracy (2024-2027), CRPF (2024-2028), BaseCamp Zero (2022-2026), EcoPipe (2023-2025), APAX (2022-2024). Scientific Awards: MSR 2025 Distinguished Dataset Award IEEE TCSE Distinguished Paper Award (SANER 2022) ICSE 2022 Best Artifact Award SCAM 2022 Best Artifact Award Conference Leadership: General Chair of SCAM 2024, Program Co-Chair for GPCE 2024, and active in organizing summer schools on security testing.

Blake Saurelsمشاهده پروفایل

پژوهشگر ارشد

• Visual Perception
• Cognitive Neuroscience
• Neural Correlates of Vision
• +۴ مورد دیگر

Dr. Blake Saurels is a Postdoctoral Research Fellow at the School of Psychology within the Faculty of Health, Medicine and Behavioural Sciences at The University of Queensland. He completed his PhD in Psychology at UQ in 2022 with a thesis titled 'The perceptual and neural consequences of different types of prediction'. His educational background includes a Bachelor (Honours) of Psychology with First Class Honors (Hons Class 1A) from The University of Queensland. Dr. Saurels' research focuses on visual perception, neural correlates of vision, and cognitive neuroscience. His work examines predictive processing in the brain, face recognition mechanisms, binocular rivalry phenomena, and visual imagery. His recent publications reveal a strong interest in how the brain processes visual information, particularly regarding face pareidolia, neural prediction errors, and the relationship between visual imagery and perception. Analysis of his 15 most recent publications shows consistent exploration of neural and perceptual mechanisms related to prediction, visual processing, and imagery. His work frequently employs EEG methodology and experimental paradigms to examine how expectations shape perception and neural responses. Dr. Saurels is currently available for supervision and serves as an Associate Advisor for a PhD project titled 'The Neural Encoding of Naturalistic Faces for Social Perception,' working alongside Dr. Amanda Robinson, Associate Professor Alan Pegna, and Associate Professor Jess Taubert.

• Decentralized Machine Learning
• Federated Learning
• Industry 4.0
• +۳ مورد دیگر

Alessio Mora is a postdoctoral researcher at the Department of Computer Science and Engineering (DISI), University of Bologna, specializing in decentralized learning techniques such as Federated Learning. His work focuses on communication efficiency, heterogeneous data handling, and distributed AI systems. PhD in Computer Science and Engineering, University of Bologna (2023) Master in Computer Engineering, University of Bologna (2019) Bachelor in Computer Engineering, University of Bologna (2016) His research spans decentralized learning, federated learning, and their applications in Industry 4.0, IoT, and edge computing. Key themes include model optimization, communication efficiency, and heterogeneous data distribution in cross-device settings. Recent publications highlight trends in federated learning infrastructure, including unlearning methods, knowledge distillation, and sparse compression techniques. His work integrates theoretical analysis with practical implementations in industrial and IoT contexts. Summer of Reproducibility Award – Flower Labs (2023) Awarded $2,000 USD for implementing and reproducing the FedMLB baseline in Flower, advancing reproducibility in federated learning. Mora has contributed to European projects like OntoTrans and SimDOME, focusing on ontology-based systems for materials modeling and industrial data translation. His technical expertise includes Docker, Stardog triplestore, FastAPI, and Python wrappers for chemical simulation tools.